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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPObH0Gl3ac/6R9KAmdeEZ7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DanielWarfield1/MLWritingAndResearch/blob/main/GeneticAlgorithm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining Problem 1"
      ],
      "metadata": {
        "id": "HHlLURUJl7vM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "yDd4AGCql5u8"
      },
      "outputs": [],
      "source": [
        "teachers = {\n",
        "    'Morgan': ['math', 'physics', 'statistics', 'computer science'],\n",
        "    'Jordan': ['english', 'creative writing', 'drama', 'debate'],\n",
        "    'Casey': ['biology', 'chemistry', 'psychology', 'sociology'],\n",
        "    'Riley': ['history', 'geography', 'economics', 'business'],\n",
        "    'Quinn': ['art', 'music', 'theater', 'french', 'german', 'spanish'],\n",
        "    'Taylor': ['philosophy', 'ethics', 'english', 'health', 'physical education'],\n",
        "    'Alex': ['robotics', 'engineering', 'environmental science']\n",
        "}\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = {\n",
        "    'physics1': {'type': 'physics', 'weekly_instances': 3, 'requires_media': False, 'requires_computers': False, 'requires_chemlab': False, 'class_size': 20},\n",
        "    'modern sculpture': {'type': 'art', 'weekly_instances': 2, 'requires_media': False, 'requires_computers': False, 'requires_chemlab': False, 'class_size': 15},\n",
        "    'jazz': {'type': 'music', 'weekly_instances': 2, 'requires_media': True, 'requires_computers': False, 'requires_chemlab': False, 'class_size': 16},\n",
        "    'calculus AB': {'type': 'math', 'weekly_instances': 3, 'requires_media': False, 'requires_computers': False, 'requires_chemlab': False, 'class_size': 18},\n",
        "    'british literature': {'type': 'english', 'weekly_instances': 3, 'requires_media': False, 'requires_computers': False, 'requires_chemlab': False, 'class_size': 22},\n",
        "    'french 1': {'type': 'french', 'weekly_instances': 2, 'requires_media': True, 'requires_computers': False, 'requires_chemlab': False, 'class_size': 17},\n",
        "    'intro to psychology': {'type': 'psychology', 'weekly_instances': 2, 'requires_media': False, 'requires_computers': True, 'requires_chemlab': False, 'class_size': 20},\n",
        "    'principles of sociology': {'type': 'sociology', 'weekly_instances': 2, 'requires_media': False, 'requires_computers': True, 'requires_chemlab': False, 'class_size': 19},\n",
        "    'introduction to programming': {'type': 'computer science', 'weekly_instances': 3, 'requires_media': False, 'requires_computers': True, 'requires_chemlab': False, 'class_size': 20},\n",
        "    'AP biology': {'type': 'biology', 'weekly_instances': 3, 'requires_media': False, 'requires_computers': False, 'requires_chemlab': True, 'class_size': 20},\n",
        "    'chemistry fundamentals': {'type': 'chemistry', 'weekly_instances': 3, 'requires_media': False, 'requires_computers': False, 'requires_chemlab': True, 'class_size': 21},\n",
        "    'debate and rhetoric': {'type': 'debate', 'weekly_instances': 2, 'requires_media': False, 'requires_computers': False, 'requires_chemlab': False, 'class_size': 18},\n",
        "    'statistics and probability': {'type': 'statistics', 'weekly_instances': 3, 'requires_media': False, 'requires_computers': True, 'requires_chemlab': False, 'class_size': 23}\n",
        "}\n"
      ],
      "metadata": {
        "id": "X6s3P9ANm-Y9"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rooms = {\n",
        "    'Room 101': {'capacity': 30, 'has_media': False, 'has_computers': False, 'is_chemlab': False},\n",
        "    'Room 102': {'capacity': 25, 'has_media': True, 'has_computers': False, 'is_chemlab': False},\n",
        "    'Room 105': {'capacity': 30, 'has_media': True, 'has_computers': True, 'is_chemlab': False},\n",
        "    'Art Studio A': {'capacity': 20, 'has_media': True, 'has_computers': False, 'is_chemlab': False},\n",
        "    'Drama Stage': {'capacity': 25, 'has_media': True, 'has_computers': False, 'is_chemlab': False},\n",
        "    'Gym A': {'capacity': 30, 'has_media': False, 'has_computers': False, 'is_chemlab': False},\n",
        "    'Computer Lab 2': {'capacity': 20, 'has_media': False, 'has_computers': True, 'is_chemlab': False},\n",
        "    'Chem Lab A': {'capacity': 20, 'has_media': False, 'has_computers': False, 'is_chemlab': True},\n",
        "    'Lecture Hall 2': {'capacity': 35, 'has_media': True, 'has_computers': False, 'is_chemlab': False},\n",
        "    'Language Lab': {'capacity': 20, 'has_media': True, 'has_computers': True, 'is_chemlab': False},\n",
        "    'Philosophy Room': {'capacity': 18, 'has_media': False, 'has_computers': False, 'is_chemlab': False}\n",
        "}"
      ],
      "metadata": {
        "id": "ua-fgtwjoMk0"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Re-Representing as chromosome"
      ],
      "metadata": {
        "id": "9pzE9QHeoeNW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Parameters\n",
        "num_periods = 8\n",
        "num_days = 5\n",
        "\n",
        "# Generate timeslots like \"Period 1_Day 1\", \"Period 2_Day 3\", etc.\n",
        "timeslots = [f'Period {p+1}_Day {d+1}' for d in range(num_days) for p in range(num_periods)]\n",
        "\n",
        "# Create list of room-timeslot pairs\n",
        "data = [{'room': room, 'timeslot': timeslot} for room in rooms.keys() for timeslot in timeslots]\n",
        "\n",
        "# Create DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "zbR9yGePogsF",
        "outputId": "0e53d59c-84e2-4346-a564-f58e4ef40c51"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                room        timeslot\n",
              "0           Room 101  Period 1_Day 1\n",
              "1           Room 101  Period 2_Day 1\n",
              "2           Room 101  Period 3_Day 1\n",
              "3           Room 101  Period 4_Day 1\n",
              "4           Room 101  Period 5_Day 1\n",
              "..               ...             ...\n",
              "435  Philosophy Room  Period 4_Day 5\n",
              "436  Philosophy Room  Period 5_Day 5\n",
              "437  Philosophy Room  Period 6_Day 5\n",
              "438  Philosophy Room  Period 7_Day 5\n",
              "439  Philosophy Room  Period 8_Day 5\n",
              "\n",
              "[440 rows x 2 columns]"
            ],
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              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>room</th>\n",
              "      <th>timeslot</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>Room 101</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <th>2</th>\n",
              "      <td>Room 101</td>\n",
              "      <td>Period 3_Day 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Room 101</td>\n",
              "      <td>Period 4_Day 1</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>Room 101</td>\n",
              "      <td>Period 5_Day 1</td>\n",
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              "      <td>Philosophy Room</td>\n",
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              "    <tr>\n",
              "      <th>437</th>\n",
              "      <td>Philosophy Room</td>\n",
              "      <td>Period 6_Day 5</td>\n",
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              "      <td>Philosophy Room</td>\n",
              "      <td>Period 7_Day 5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>439</th>\n",
              "      <td>Philosophy Room</td>\n",
              "      <td>Period 8_Day 5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>440 rows × 2 columns</p>\n",
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              "    border-color: transparent;\n",
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              "\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 440,\n  \"fields\": [\n    {\n      \"column\": \"room\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"Gym A\",\n          \"Room 101\",\n          \"Language Lab\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"timeslot\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          \"Period 4_Day 3\",\n          \"Period 1_Day 3\",\n          \"Period 8_Day 2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating Random Population Member"
      ],
      "metadata": {
        "id": "phf8z11hs-Mx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "def create_population_member():\n",
        "\n",
        "    # Pull class names and teacher names\n",
        "    class_names = list(classes.keys())\n",
        "    teacher_names = list(teachers.keys())\n",
        "\n",
        "    # Define probability of assigning None vs a valid entry\n",
        "    none_prob = 0.2  # 20% chance of being None\n",
        "\n",
        "    # Randomly assign class and teacher\n",
        "    df['class'] = [random.choice(class_names + [None] * int(len(class_names) * none_prob)) for _ in range(len(df))]\n",
        "    df['teacher'] = [random.choice(teacher_names + [None] * int(len(teacher_names) * none_prob)) for _ in range(len(df))]\n",
        "\n",
        "    return df.copy(deep=True)\n",
        "\n",
        "create_population_member()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "HaM-YUlgpJXk",
        "outputId": "8221f55d-65e6-47b4-c3c5-faa578f4763f"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                room        timeslot                       class teacher\n",
              "0           Room 101  Period 1_Day 1                    french 1  Taylor\n",
              "1           Room 101  Period 2_Day 1     principles of sociology    None\n",
              "2           Room 101  Period 3_Day 1      chemistry fundamentals  Morgan\n",
              "3           Room 101  Period 4_Day 1         debate and rhetoric  Taylor\n",
              "4           Room 101  Period 5_Day 1                        None  Taylor\n",
              "..               ...             ...                         ...     ...\n",
              "435  Philosophy Room  Period 4_Day 5                    physics1   Riley\n",
              "436  Philosophy Room  Period 5_Day 5                        jazz  Morgan\n",
              "437  Philosophy Room  Period 6_Day 5  statistics and probability   Casey\n",
              "438  Philosophy Room  Period 7_Day 5      chemistry fundamentals   Casey\n",
              "439  Philosophy Room  Period 8_Day 5                        None  Jordan\n",
              "\n",
              "[440 rows x 4 columns]"
            ],
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Room 101</td>\n",
              "      <td>Period 2_Day 1</td>\n",
              "      <td>principles of sociology</td>\n",
              "      <td>None</td>\n",
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              "    <tr>\n",
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              "      <td>Period 3_Day 1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>debate and rhetoric</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>Riley</td>\n",
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              "      <th>436</th>\n",
              "      <td>Philosophy Room</td>\n",
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              "    <tr>\n",
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              "      <td>Philosophy Room</td>\n",
              "      <td>Period 6_Day 5</td>\n",
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              "      <th>438</th>\n",
              "      <td>Philosophy Room</td>\n",
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              "    <tr>\n",
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              "      <td>Philosophy Room</td>\n",
              "      <td>Period 8_Day 5</td>\n",
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              "      <td>Jordan</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>440 rows × 4 columns</p>\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-09fff57d-e9d1-4bfd-b6e0-f9f18f096710 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"create_population_member()\",\n  \"rows\": 440,\n  \"fields\": [\n    {\n      \"column\": \"room\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"Gym A\",\n          \"Room 101\",\n          \"Language Lab\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"timeslot\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          \"Period 4_Day 3\",\n          \"Period 1_Day 3\",\n          \"Period 8_Day 2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"jazz\",\n          \"AP biology\",\n          \"french 1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"teacher\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Taylor\",\n          \"Morgan\",\n          \"Casey\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating Population"
      ],
      "metadata": {
        "id": "wAvXT6_htYou"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "population = [create_population_member() for _ in range(100)]"
      ],
      "metadata": {
        "id": "ptVpUEoytYNp"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Selection"
      ],
      "metadata": {
        "id": "ybSV0HTtti7L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def fitness(chromosome):\n",
        "    penalty = 0\n",
        "\n",
        "    teacher_timeslot_set = set()\n",
        "    class_instance_counter = {}\n",
        "\n",
        "    for idx, row in chromosome.iterrows():\n",
        "        class_name = row['class']\n",
        "        teacher = row['teacher']\n",
        "        room = row['room']\n",
        "        timeslot = row['timeslot']\n",
        "\n",
        "        if class_name is None or teacher is None:\n",
        "            continue  # No assignment, skip\n",
        "\n",
        "        # Track how many times this class is scheduled\n",
        "        class_instance_counter[class_name] = class_instance_counter.get(class_name, 0) + 1\n",
        "\n",
        "        # 1. Teacher qualified?\n",
        "        class_info = classes[class_name]\n",
        "        class_type = class_info['type']\n",
        "        if class_type not in teachers.get(teacher, []):\n",
        "            penalty += 10\n",
        "\n",
        "        # 2. Room resources\n",
        "        room_info = rooms[room]\n",
        "        if class_info['requires_media'] and not room_info['has_media']:\n",
        "            penalty += 5\n",
        "        if class_info['requires_computers'] and not room_info['has_computers']:\n",
        "            penalty += 5\n",
        "        if class_info['requires_chemlab'] and not room_info['is_chemlab']:\n",
        "            penalty += 5\n",
        "\n",
        "        # 3. Room capacity\n",
        "        if room_info['capacity'] < class_info['class_size']:\n",
        "            penalty += (class_info['class_size'] - room_info['capacity'])\n",
        "\n",
        "        # 4. Double-booked teacher\n",
        "        key = (teacher, timeslot)\n",
        "        if key in teacher_timeslot_set:\n",
        "            penalty += 15\n",
        "        else:\n",
        "            teacher_timeslot_set.add(key)\n",
        "\n",
        "    # 5. Penalize over/under-scheduling of classes\n",
        "    for class_name, class_info in classes.items():\n",
        "        required = class_info['weekly_instances']\n",
        "        actual = class_instance_counter.get(class_name, 0)\n",
        "        if actual != required:\n",
        "            penalty += abs(required - actual) * 5  # Penalty per instance off\n",
        "\n",
        "    return -penalty\n",
        "\n",
        "fitness(population[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1sw8w1Ntg93",
        "outputId": "ed6c285d-dff2-4866-ca8a-bb29519685fa"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-7807"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def selection(population):\n",
        "    # Evaluate fitness for each chromosome\n",
        "    scored_population = [\n",
        "        (chromosome, fitness(chromosome))\n",
        "        for chromosome in population\n",
        "    ]\n",
        "\n",
        "    # Sort by fitness (higher is better)\n",
        "    scored_population.sort(key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # Keep top 50%\n",
        "    num_survivors = len(population) // 2\n",
        "    survivors = [chromosome for chromosome, _ in scored_population[:num_survivors]]\n",
        "\n",
        "    return survivors\n",
        "\n",
        "survivors = selection(population)\n",
        "len(survivors)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rIUXrDZvuGtu",
        "outputId": "b4437198-99e4-49ce-bdad-1c93a7a00dd9"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Crossover"
      ],
      "metadata": {
        "id": "EYfF6W3tu0cA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "def crossover(survivors, crossover_rate=0.5):\n",
        "    original_population_size = len(survivors) * 2\n",
        "    offspring = []\n",
        "\n",
        "    while len(offspring) < len(survivors):\n",
        "        # Pick two distinct parents\n",
        "        parent1, parent2 = random.sample(survivors, 2)\n",
        "\n",
        "        # Create a new child DataFrame\n",
        "        child = parent1.copy()\n",
        "        for idx in child.index:\n",
        "            if random.random() < crossover_rate:\n",
        "                child.at[idx, 'class'] = parent1.at[idx, 'class']\n",
        "                child.at[idx, 'teacher'] = parent1.at[idx, 'teacher']\n",
        "            else:\n",
        "                child.at[idx, 'class'] = parent2.at[idx, 'class']\n",
        "                child.at[idx, 'teacher'] = parent2.at[idx, 'teacher']\n",
        "\n",
        "        offspring.append(child)\n",
        "\n",
        "    return survivors + offspring\n",
        "\n",
        "new_population = crossover(survivors)\n",
        "len(new_population)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-R0fveLul4e",
        "outputId": "1d63a4b2-6de3-44a0-b33b-42f6a19a83c9"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mutation"
      ],
      "metadata": {
        "id": "_ZQWWTXDvcuy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "def mutate(population, mutation_rate=0.01):\n",
        "    class_names = list(classes.keys())\n",
        "    teacher_names = list(teachers.keys())\n",
        "\n",
        "    for chromosome in population:\n",
        "        for idx in chromosome.index:\n",
        "            # Mutate class\n",
        "            if random.random() < mutation_rate:\n",
        "                chromosome.at[idx, 'class'] = random.choice(class_names + [None])\n",
        "\n",
        "            # Mutate teacher\n",
        "            if random.random() < mutation_rate:\n",
        "                chromosome.at[idx, 'teacher'] = random.choice(teacher_names + [None])\n",
        "\n",
        "    return population\n",
        "\n",
        "mutated_population = mutate(new_population)\n",
        "len(mutated_population)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcW_Y_LyvclW",
        "outputId": "546b3cf8-cc10-43c9-bfc2-eaa8f6dc4b4e"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "def mutate(population, mutation_rate=0.01):\n",
        "    class_names = list(classes.keys())\n",
        "\n",
        "    for chromosome in population:\n",
        "        for idx in chromosome.index:\n",
        "            # Mutate class and teacher for a given classroom timeslot\n",
        "            if random.random() < mutation_rate:\n",
        "                new_class = random.choice(class_names + [None])\n",
        "                chromosome.at[idx, 'class'] = new_class\n",
        "\n",
        "                if new_class is None:\n",
        "                    possible_teachers = list(teachers.keys()) + [None]\n",
        "                else:\n",
        "                    class_type = classes[new_class]['type']\n",
        "                    possible_teachers = [\n",
        "                        t for t, qualified_types in teachers.items()\n",
        "                        if class_type in qualified_types\n",
        "                    ] + [None]\n",
        "\n",
        "                if possible_teachers:\n",
        "                    chromosome.at[idx, 'teacher'] = random.choice(possible_teachers)\n",
        "\n",
        "    return population\n",
        "\n",
        "\n",
        "mutated_population = mutate(new_population)\n",
        "len(mutated_population)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYmQO0Mj8gz3",
        "outputId": "76d57b2e-75c1-4d6c-a6d0-8399cbeed691"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Optimizing Schedule"
      ],
      "metadata": {
        "id": "fwOcoczrvrDY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm\n",
        "\n",
        "# --- Set up GA loop ---\n",
        "population_size = 40\n",
        "generations = 300\n",
        "\n",
        "# Assume df is a DataFrame of room-timeslot pairs\n",
        "df_template = df[['room', 'timeslot']].copy()  # reuse your earlier structure\n",
        "\n",
        "population = [create_population_member() for _ in range(population_size)]\n",
        "\n",
        "avg_fitness = []\n",
        "min_fitness = []\n",
        "max_fitness = []\n",
        "\n",
        "for gen in tqdm(range(generations), desc=\"Evolving\", dynamic_ncols=True):\n",
        "    scores = [fitness(chrom) for chrom in population]\n",
        "\n",
        "    avg = sum(scores) / len(scores)\n",
        "    mn = min(scores)\n",
        "    mx = max(scores)\n",
        "\n",
        "    avg_fitness.append(avg)\n",
        "    min_fitness.append(mn)\n",
        "    max_fitness.append(mx)\n",
        "\n",
        "    tqdm.write(f\"Gen {gen} — avg: {avg:.1f}, min: {mn:.1f}, max: {mx:.1f}\")\n",
        "\n",
        "    survivors = selection(population)\n",
        "    population = crossover(survivors, crossover_rate=0.7)\n",
        "    population = mutate(population, mutation_rate=0.005)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKrW_1GAvVum",
        "outputId": "1fe29220-5cce-4dd8-d37f-af75bc3ae9e8"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   0%|          | 0/300 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 0 — avg: -7204.1, min: -7880.0, max: -6822.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   0%|          | 1/300 [00:02<10:00,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 1 — avg: -7001.2, min: -7377.0, max: -6636.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   1%|          | 2/300 [00:05<11:38,  2.34s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 2 — avg: -6863.1, min: -7465.0, max: -6519.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   1%|          | 3/300 [00:07<11:41,  2.36s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 3 — avg: -6708.1, min: -6987.0, max: -6489.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   1%|▏         | 4/300 [00:09<10:55,  2.22s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 4 — avg: -6594.7, min: -6932.0, max: -6301.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   2%|▏         | 5/300 [00:11<10:26,  2.12s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 5 — avg: -6476.3, min: -6758.0, max: -6246.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   2%|▏         | 6/300 [00:13<10:11,  2.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 6 — avg: -6364.1, min: -6606.0, max: -5864.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   2%|▏         | 7/300 [00:15<10:00,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 7 — avg: -6274.9, min: -6651.0, max: -5849.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   3%|▎         | 8/300 [00:18<10:54,  2.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 8 — avg: -6165.7, min: -6473.0, max: -5824.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   3%|▎         | 9/300 [00:20<10:57,  2.26s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 9 — avg: -6046.0, min: -6253.0, max: -5738.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   3%|▎         | 10/300 [00:22<10:31,  2.18s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 10 — avg: -5930.1, min: -6226.0, max: -5720.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   4%|▎         | 11/300 [00:24<10:09,  2.11s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 11 — avg: -5805.8, min: -6112.0, max: -5423.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   4%|▍         | 12/300 [00:26<09:58,  2.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 12 — avg: -5680.1, min: -5968.0, max: -5408.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   4%|▍         | 13/300 [00:28<09:46,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 13 — avg: -5545.6, min: -5810.0, max: -5164.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   5%|▍         | 14/300 [00:31<10:51,  2.28s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 14 — avg: -5434.6, min: -5678.0, max: -5142.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   5%|▌         | 15/300 [00:33<10:36,  2.23s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 15 — avg: -5337.9, min: -5616.0, max: -4992.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   5%|▌         | 16/300 [00:35<10:12,  2.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 16 — avg: -5251.6, min: -5607.0, max: -4936.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   6%|▌         | 17/300 [00:37<09:52,  2.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 17 — avg: -5150.7, min: -5400.0, max: -4700.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   6%|▌         | 18/300 [00:39<09:39,  2.06s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 18 — avg: -5043.9, min: -5240.0, max: -4677.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   6%|▋         | 19/300 [00:41<09:31,  2.03s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 19 — avg: -4931.2, min: -5233.0, max: -4652.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   7%|▋         | 20/300 [00:44<10:41,  2.29s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 20 — avg: -4856.4, min: -5287.0, max: -4620.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   7%|▋         | 21/300 [00:46<10:14,  2.20s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 21 — avg: -4755.6, min: -4978.0, max: -4534.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   7%|▋         | 22/300 [00:48<09:52,  2.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 22 — avg: -4655.9, min: -4765.0, max: -4478.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   8%|▊         | 23/300 [00:50<09:36,  2.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 23 — avg: -4596.4, min: -4879.0, max: -4416.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   8%|▊         | 24/300 [00:53<09:53,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 24 — avg: -4514.0, min: -4679.0, max: -4346.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   8%|▊         | 25/300 [00:56<11:04,  2.42s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 25 — avg: -4468.8, min: -4657.0, max: -4346.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   9%|▊         | 26/300 [00:58<11:11,  2.45s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 26 — avg: -4402.8, min: -4582.0, max: -4188.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   9%|▉         | 27/300 [01:00<10:27,  2.30s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 27 — avg: -4329.9, min: -4478.0, max: -4118.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:   9%|▉         | 28/300 [01:02<09:57,  2.20s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 28 — avg: -4269.1, min: -4446.0, max: -4048.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  10%|▉         | 29/300 [01:04<09:36,  2.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 29 — avg: -4173.8, min: -4331.0, max: -4020.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  10%|█         | 30/300 [01:05<09:19,  2.07s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 30 — avg: -4114.3, min: -4301.0, max: -3917.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  10%|█         | 31/300 [01:08<09:44,  2.17s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 31 — avg: -4028.7, min: -4253.0, max: -3788.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  11%|█         | 32/300 [01:10<10:10,  2.28s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 32 — avg: -3948.6, min: -4091.0, max: -3748.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  11%|█         | 33/300 [01:12<09:42,  2.18s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 33 — avg: -3876.8, min: -4093.0, max: -3728.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  11%|█▏        | 34/300 [01:14<09:23,  2.12s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 34 — avg: -3822.5, min: -3983.0, max: -3672.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  12%|█▏        | 35/300 [01:16<09:07,  2.07s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 35 — avg: -3767.2, min: -3916.0, max: -3592.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  12%|█▏        | 36/300 [01:18<08:58,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 36 — avg: -3715.7, min: -3852.0, max: -3548.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  12%|█▏        | 37/300 [01:21<09:26,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 37 — avg: -3647.0, min: -3815.0, max: -3531.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  13%|█▎        | 38/300 [01:23<09:51,  2.26s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 38 — avg: -3594.9, min: -3781.0, max: -3423.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  13%|█▎        | 39/300 [01:25<09:26,  2.17s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 39 — avg: -3547.2, min: -3689.0, max: -3430.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  13%|█▎        | 40/300 [01:27<09:07,  2.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 40 — avg: -3474.7, min: -3570.0, max: -3248.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  14%|█▎        | 41/300 [01:29<08:54,  2.06s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 41 — avg: -3418.7, min: -3569.0, max: -3248.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  14%|█▍        | 42/300 [01:31<08:43,  2.03s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 42 — avg: -3356.0, min: -3485.0, max: -3143.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  14%|█▍        | 43/300 [01:34<09:14,  2.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 43 — avg: -3299.3, min: -3443.0, max: -3126.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  15%|█▍        | 44/300 [01:36<09:33,  2.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 44 — avg: -3241.7, min: -3322.0, max: -3088.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  15%|█▌        | 45/300 [01:38<09:08,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 45 — avg: -3212.1, min: -3381.0, max: -3088.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  15%|█▌        | 46/300 [01:40<08:53,  2.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 46 — avg: -3140.1, min: -3241.0, max: -2974.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  16%|█▌        | 47/300 [01:42<08:40,  2.06s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 47 — avg: -3087.8, min: -3183.0, max: -2949.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  16%|█▌        | 48/300 [01:44<08:30,  2.03s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 48 — avg: -3044.2, min: -3160.0, max: -2884.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  16%|█▋        | 49/300 [01:47<09:00,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 49 — avg: -3013.1, min: -3177.0, max: -2854.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  17%|█▋        | 50/300 [01:49<09:19,  2.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 50 — avg: -2949.2, min: -3077.0, max: -2825.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  17%|█▋        | 51/300 [01:51<08:55,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 51 — avg: -2917.2, min: -3015.0, max: -2825.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  17%|█▋        | 52/300 [01:53<08:40,  2.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 52 — avg: -2871.3, min: -2999.0, max: -2755.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  18%|█▊        | 53/300 [01:54<08:27,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 53 — avg: -2826.1, min: -2965.0, max: -2713.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  18%|█▊        | 54/300 [01:56<08:18,  2.03s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 54 — avg: -2777.9, min: -2889.0, max: -2682.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  18%|█▊        | 55/300 [01:59<08:49,  2.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 55 — avg: -2723.1, min: -2866.0, max: -2550.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  19%|█▊        | 56/300 [02:01<09:06,  2.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 56 — avg: -2682.0, min: -2793.0, max: -2525.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  19%|█▉        | 57/300 [02:03<08:45,  2.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 57 — avg: -2643.0, min: -2794.0, max: -2495.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  19%|█▉        | 58/300 [02:05<08:30,  2.11s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 58 — avg: -2598.2, min: -2792.0, max: -2468.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  20%|█▉        | 59/300 [02:07<08:16,  2.06s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 59 — avg: -2548.6, min: -2673.0, max: -2438.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  20%|██        | 60/300 [02:09<08:07,  2.03s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 60 — avg: -2505.0, min: -2627.0, max: -2367.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  20%|██        | 61/300 [02:12<08:46,  2.20s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 61 — avg: -2460.6, min: -2598.0, max: -2318.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  21%|██        | 62/300 [02:14<08:53,  2.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 62 — avg: -2428.5, min: -2525.0, max: -2308.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  21%|██        | 63/300 [02:16<08:31,  2.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 63 — avg: -2398.2, min: -2496.0, max: -2231.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  21%|██▏       | 64/300 [02:18<08:15,  2.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 64 — avg: -2362.8, min: -2479.0, max: -2203.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  22%|██▏       | 65/300 [02:20<08:03,  2.06s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 65 — avg: -2318.7, min: -2451.0, max: -2212.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  22%|██▏       | 66/300 [02:22<07:55,  2.03s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 66 — avg: -2277.8, min: -2372.0, max: -2182.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  22%|██▏       | 67/300 [02:25<08:34,  2.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 67 — avg: -2253.4, min: -2366.0, max: -2123.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  23%|██▎       | 68/300 [02:27<08:38,  2.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 68 — avg: -2234.8, min: -2404.0, max: -2110.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  23%|██▎       | 69/300 [02:29<08:17,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 69 — avg: -2183.5, min: -2259.0, max: -2089.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  23%|██▎       | 70/300 [02:31<07:59,  2.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 70 — avg: -2139.4, min: -2348.0, max: -1990.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  24%|██▎       | 71/300 [02:33<07:47,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 71 — avg: -2083.8, min: -2220.0, max: -1865.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  24%|██▍       | 72/300 [02:35<07:41,  2.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 72 — avg: -2060.4, min: -2250.0, max: -1865.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  24%|██▍       | 73/300 [02:38<08:21,  2.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 73 — avg: -2015.4, min: -2161.0, max: -1865.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  25%|██▍       | 74/300 [02:40<08:21,  2.22s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 74 — avg: -1989.3, min: -2104.0, max: -1865.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  25%|██▌       | 75/300 [02:42<08:03,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 75 — avg: -1961.2, min: -2087.0, max: -1865.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  25%|██▌       | 76/300 [02:44<07:48,  2.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 76 — avg: -1928.6, min: -2048.0, max: -1830.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  26%|██▌       | 77/300 [02:46<07:37,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 77 — avg: -1904.2, min: -1999.0, max: -1825.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  26%|██▌       | 78/300 [02:48<07:28,  2.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 78 — avg: -1875.2, min: -1989.0, max: -1761.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  26%|██▋       | 79/300 [02:50<08:07,  2.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 79 — avg: -1846.2, min: -1959.0, max: -1763.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  27%|██▋       | 80/300 [02:52<08:05,  2.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 80 — avg: -1824.9, min: -1921.0, max: -1748.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  27%|██▋       | 81/300 [02:54<07:46,  2.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 81 — avg: -1794.5, min: -1912.0, max: -1671.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  27%|██▋       | 82/300 [02:56<07:33,  2.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 82 — avg: -1776.5, min: -1905.0, max: -1656.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  28%|██▊       | 83/300 [02:58<07:23,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 83 — avg: -1760.3, min: -1873.0, max: -1663.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  28%|██▊       | 84/300 [03:00<07:15,  2.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 84 — avg: -1724.7, min: -1887.0, max: -1611.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  28%|██▊       | 85/300 [03:03<07:53,  2.20s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 85 — avg: -1693.2, min: -1796.0, max: -1606.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  29%|██▊       | 86/300 [03:05<07:52,  2.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 86 — avg: -1659.5, min: -1792.0, max: -1552.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  29%|██▉       | 87/300 [03:07<07:34,  2.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 87 — avg: -1635.5, min: -1743.0, max: -1541.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  29%|██▉       | 88/300 [03:09<07:20,  2.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 88 — avg: -1627.5, min: -1730.0, max: -1535.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  30%|██▉       | 89/300 [03:11<07:11,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 89 — avg: -1609.5, min: -1727.0, max: -1530.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  30%|███       | 90/300 [03:13<07:03,  2.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 90 — avg: -1584.7, min: -1657.0, max: -1524.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  30%|███       | 91/300 [03:16<07:40,  2.20s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 91 — avg: -1559.0, min: -1616.0, max: -1460.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  31%|███       | 92/300 [03:18<07:39,  2.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 92 — avg: -1535.8, min: -1727.0, max: -1389.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  31%|███       | 93/300 [03:20<07:20,  2.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 93 — avg: -1501.6, min: -1603.0, max: -1390.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  31%|███▏      | 94/300 [03:22<07:07,  2.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 94 — avg: -1478.3, min: -1579.0, max: -1345.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  32%|███▏      | 95/300 [03:24<06:57,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 95 — avg: -1451.7, min: -1537.0, max: -1345.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  32%|███▏      | 96/300 [03:26<06:50,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 96 — avg: -1430.8, min: -1545.0, max: -1317.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  32%|███▏      | 97/300 [03:28<07:25,  2.19s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 97 — avg: -1393.6, min: -1494.0, max: -1302.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  33%|███▎      | 98/300 [03:30<07:26,  2.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 98 — avg: -1367.0, min: -1448.0, max: -1279.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  33%|███▎      | 99/300 [03:32<07:07,  2.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 99 — avg: -1330.8, min: -1401.0, max: -1221.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  33%|███▎      | 100/300 [03:34<06:55,  2.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 100 — avg: -1308.0, min: -1423.0, max: -1157.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  34%|███▎      | 101/300 [03:36<06:45,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 101 — avg: -1282.5, min: -1432.0, max: -1157.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  34%|███▍      | 102/300 [03:38<06:37,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 102 — avg: -1263.1, min: -1378.0, max: -1157.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  34%|███▍      | 103/300 [03:41<07:10,  2.19s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 103 — avg: -1229.9, min: -1341.0, max: -1131.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  35%|███▍      | 104/300 [03:43<07:13,  2.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 104 — avg: -1209.2, min: -1321.0, max: -1128.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  35%|███▌      | 105/300 [03:45<06:55,  2.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 105 — avg: -1179.9, min: -1264.0, max: -1104.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  35%|███▌      | 106/300 [03:47<06:43,  2.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 106 — avg: -1154.6, min: -1246.0, max: -1057.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  36%|███▌      | 107/300 [03:49<06:33,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 107 — avg: -1124.5, min: -1191.0, max: -984.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  36%|███▌      | 108/300 [03:51<06:24,  2.00s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 108 — avg: -1106.0, min: -1199.0, max: -984.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  36%|███▋      | 109/300 [03:54<06:51,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 109 — avg: -1083.4, min: -1184.0, max: -992.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  37%|███▋      | 110/300 [03:56<06:59,  2.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 110 — avg: -1060.5, min: -1187.0, max: -949.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  37%|███▋      | 111/300 [03:58<06:41,  2.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 111 — avg: -1038.1, min: -1127.0, max: -934.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  37%|███▋      | 112/300 [03:59<06:29,  2.07s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 112 — avg: -1012.8, min: -1096.0, max: -914.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  38%|███▊      | 113/300 [04:01<06:20,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 113 — avg: -996.1, min: -1101.0, max: -913.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  38%|███▊      | 114/300 [04:03<06:13,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 114 — avg: -976.2, min: -1078.0, max: -887.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  38%|███▊      | 115/300 [04:06<06:39,  2.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 115 — avg: -967.8, min: -1093.0, max: -887.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  39%|███▊      | 116/300 [04:08<06:48,  2.22s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 116 — avg: -946.5, min: -1034.0, max: -859.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  39%|███▉      | 117/300 [04:10<06:32,  2.14s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 117 — avg: -930.3, min: -990.0, max: -854.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  39%|███▉      | 118/300 [04:12<06:19,  2.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 118 — avg: -913.3, min: -994.0, max: -811.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  40%|███▉      | 119/300 [04:14<06:09,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 119 — avg: -895.5, min: -957.0, max: -803.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  40%|████      | 120/300 [04:16<06:02,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 120 — avg: -877.0, min: -943.0, max: -800.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  40%|████      | 121/300 [04:19<06:27,  2.17s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 121 — avg: -864.0, min: -929.0, max: -803.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  41%|████      | 122/300 [04:21<06:35,  2.22s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 122 — avg: -855.2, min: -901.0, max: -803.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  41%|████      | 123/300 [04:23<06:18,  2.14s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 123 — avg: -851.0, min: -920.0, max: -797.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  41%|████▏     | 124/300 [04:25<06:04,  2.07s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 124 — avg: -844.7, min: -904.0, max: -742.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  42%|████▏     | 125/300 [04:27<05:57,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 125 — avg: -839.1, min: -929.0, max: -742.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  42%|████▏     | 126/300 [04:29<05:48,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 126 — avg: -821.0, min: -904.0, max: -752.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  42%|████▏     | 127/300 [04:32<06:19,  2.20s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 127 — avg: -812.6, min: -896.0, max: -760.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  43%|████▎     | 128/300 [04:35<07:16,  2.54s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 128 — avg: -802.1, min: -869.0, max: -755.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  43%|████▎     | 129/300 [04:37<06:43,  2.36s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 129 — avg: -790.9, min: -848.0, max: -732.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  43%|████▎     | 130/300 [04:39<06:19,  2.23s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 130 — avg: -781.4, min: -853.0, max: -698.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  44%|████▎     | 131/300 [04:40<06:03,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 131 — avg: -762.8, min: -823.0, max: -680.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  44%|████▍     | 132/300 [04:42<05:50,  2.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 132 — avg: -742.9, min: -797.0, max: -673.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  44%|████▍     | 133/300 [04:45<05:58,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 133 — avg: -733.5, min: -785.0, max: -680.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  45%|████▍     | 134/300 [04:47<06:17,  2.27s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 134 — avg: -715.6, min: -789.0, max: -667.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  45%|████▌     | 135/300 [04:49<05:58,  2.17s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 135 — avg: -702.4, min: -763.0, max: -627.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  45%|████▌     | 136/300 [04:51<05:44,  2.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 136 — avg: -694.7, min: -763.0, max: -622.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  46%|████▌     | 137/300 [04:53<05:34,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 137 — avg: -684.5, min: -794.0, max: -619.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  46%|████▌     | 138/300 [04:55<05:25,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 138 — avg: -676.5, min: -808.0, max: -619.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  46%|████▋     | 139/300 [04:58<05:36,  2.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 139 — avg: -664.4, min: -744.0, max: -598.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  47%|████▋     | 140/300 [05:00<05:59,  2.25s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 140 — avg: -655.1, min: -739.0, max: -598.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  47%|████▋     | 141/300 [05:02<05:42,  2.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 141 — avg: -635.1, min: -707.0, max: -590.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  47%|████▋     | 142/300 [05:04<05:30,  2.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 142 — avg: -625.2, min: -702.0, max: -565.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  48%|████▊     | 143/300 [05:06<05:20,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 143 — avg: -614.3, min: -669.0, max: -557.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  48%|████▊     | 144/300 [05:08<05:14,  2.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 144 — avg: -608.2, min: -701.0, max: -548.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  48%|████▊     | 145/300 [05:10<05:22,  2.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 145 — avg: -604.4, min: -669.0, max: -531.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  49%|████▊     | 146/300 [05:13<05:48,  2.26s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 146 — avg: -589.0, min: -667.0, max: -490.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  49%|████▉     | 147/300 [05:14<05:30,  2.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 147 — avg: -571.1, min: -625.0, max: -479.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  49%|████▉     | 148/300 [05:16<05:18,  2.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 148 — avg: -555.9, min: -643.0, max: -461.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  50%|████▉     | 149/300 [05:18<05:10,  2.06s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 149 — avg: -544.0, min: -620.0, max: -488.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  50%|█████     | 150/300 [05:20<05:03,  2.03s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 150 — avg: -515.3, min: -567.0, max: -448.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  50%|█████     | 151/300 [05:23<05:10,  2.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 151 — avg: -512.8, min: -579.0, max: -465.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  51%|█████     | 152/300 [05:25<05:33,  2.26s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 152 — avg: -500.8, min: -560.0, max: -427.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  51%|█████     | 153/300 [05:27<05:19,  2.17s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 153 — avg: -490.0, min: -553.0, max: -438.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  51%|█████▏    | 154/300 [05:29<05:07,  2.11s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 154 — avg: -482.9, min: -544.0, max: -437.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  52%|█████▏    | 155/300 [05:31<04:57,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 155 — avg: -468.1, min: -527.0, max: -419.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  52%|█████▏    | 156/300 [05:33<04:50,  2.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 156 — avg: -459.5, min: -505.0, max: -418.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  52%|█████▏    | 157/300 [05:36<04:55,  2.06s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 157 — avg: -455.3, min: -528.0, max: -413.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  53%|█████▎    | 158/300 [05:38<05:18,  2.25s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 158 — avg: -449.8, min: -541.0, max: -398.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  53%|█████▎    | 159/300 [05:40<05:03,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 159 — avg: -442.7, min: -519.0, max: -407.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  53%|█████▎    | 160/300 [05:42<04:52,  2.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 160 — avg: -436.9, min: -534.0, max: -362.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  54%|█████▎    | 161/300 [05:44<04:43,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 161 — avg: -431.9, min: -500.0, max: -362.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  54%|█████▍    | 162/300 [05:46<04:37,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 162 — avg: -422.8, min: -485.0, max: -353.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  54%|█████▍    | 163/300 [05:48<04:41,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 163 — avg: -417.4, min: -475.0, max: -348.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  55%|█████▍    | 164/300 [05:50<05:05,  2.25s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 164 — avg: -404.7, min: -481.0, max: -348.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  55%|█████▌    | 165/300 [05:52<04:49,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 165 — avg: -387.7, min: -464.0, max: -325.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  55%|█████▌    | 166/300 [05:54<04:39,  2.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 166 — avg: -367.2, min: -435.0, max: -297.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  56%|█████▌    | 167/300 [05:56<04:31,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 167 — avg: -359.3, min: -421.0, max: -285.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  56%|█████▌    | 168/300 [05:58<04:25,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 168 — avg: -349.0, min: -406.0, max: -285.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  56%|█████▋    | 169/300 [06:01<04:25,  2.03s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 169 — avg: -342.8, min: -459.0, max: -294.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  57%|█████▋    | 170/300 [06:03<04:51,  2.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 170 — avg: -331.7, min: -404.0, max: -278.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  57%|█████▋    | 171/300 [06:05<04:37,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 171 — avg: -321.3, min: -368.0, max: -278.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  57%|█████▋    | 172/300 [06:07<04:27,  2.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 172 — avg: -316.1, min: -355.0, max: -269.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  58%|█████▊    | 173/300 [06:09<04:19,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 173 — avg: -308.4, min: -342.0, max: -278.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  58%|█████▊    | 174/300 [06:11<04:14,  2.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 174 — avg: -311.9, min: -365.0, max: -273.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  58%|█████▊    | 175/300 [06:13<04:11,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 175 — avg: -311.5, min: -383.0, max: -273.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  59%|█████▊    | 176/300 [06:16<04:39,  2.26s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 176 — avg: -302.7, min: -336.0, max: -253.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  59%|█████▉    | 177/300 [06:18<04:25,  2.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 177 — avg: -292.0, min: -348.0, max: -234.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  59%|█████▉    | 178/300 [06:19<04:15,  2.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 178 — avg: -281.6, min: -338.0, max: -207.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  60%|█████▉    | 179/300 [06:21<04:07,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 179 — avg: -273.6, min: -321.0, max: -212.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  60%|██████    | 180/300 [06:23<04:01,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 180 — avg: -272.1, min: -314.0, max: -232.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  60%|██████    | 181/300 [06:26<03:57,  1.99s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 181 — avg: -262.9, min: -317.0, max: -202.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  61%|██████    | 182/300 [06:28<04:26,  2.25s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 182 — avg: -253.2, min: -307.0, max: -202.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  61%|██████    | 183/300 [06:30<04:13,  2.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 183 — avg: -251.7, min: -327.0, max: -211.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  61%|██████▏   | 184/300 [06:32<04:03,  2.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 184 — avg: -246.9, min: -293.0, max: -211.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  62%|██████▏   | 185/300 [06:34<03:55,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 185 — avg: -241.7, min: -294.0, max: -200.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  62%|██████▏   | 186/300 [06:36<03:49,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 186 — avg: -233.7, min: -305.0, max: -190.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  62%|██████▏   | 187/300 [06:38<03:45,  1.99s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 187 — avg: -226.9, min: -274.0, max: -190.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  63%|██████▎   | 188/300 [06:41<04:14,  2.27s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 188 — avg: -220.4, min: -286.0, max: -170.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  63%|██████▎   | 189/300 [06:43<04:01,  2.17s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 189 — avg: -210.7, min: -246.0, max: -170.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  63%|██████▎   | 190/300 [06:45<03:50,  2.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 190 — avg: -201.9, min: -237.0, max: -163.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  64%|██████▎   | 191/300 [06:47<03:43,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 191 — avg: -200.8, min: -263.0, max: -163.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  64%|██████▍   | 192/300 [06:49<03:37,  2.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 192 — avg: -195.7, min: -232.0, max: -168.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  64%|██████▍   | 193/300 [06:51<03:32,  1.99s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 193 — avg: -193.3, min: -244.0, max: -165.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  65%|██████▍   | 194/300 [06:53<04:00,  2.26s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 194 — avg: -193.2, min: -227.0, max: -146.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  65%|██████▌   | 195/300 [06:55<03:47,  2.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 195 — avg: -190.7, min: -233.0, max: -145.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  65%|██████▌   | 196/300 [06:57<03:38,  2.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 196 — avg: -185.3, min: -236.0, max: -140.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  66%|██████▌   | 197/300 [06:59<03:30,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 197 — avg: -181.8, min: -228.0, max: -145.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  66%|██████▌   | 198/300 [07:01<03:25,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 198 — avg: -172.2, min: -233.0, max: -128.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  66%|██████▋   | 199/300 [07:03<03:20,  1.98s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 199 — avg: -164.4, min: -199.0, max: -126.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  67%|██████▋   | 200/300 [07:06<03:43,  2.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 200 — avg: -159.3, min: -199.0, max: -133.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  67%|██████▋   | 201/300 [07:08<03:34,  2.17s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 201 — avg: -160.7, min: -195.0, max: -125.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  67%|██████▋   | 202/300 [07:10<03:25,  2.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 202 — avg: -155.4, min: -198.0, max: -123.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  68%|██████▊   | 203/300 [07:12<03:19,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 203 — avg: -152.7, min: -208.0, max: -116.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  68%|██████▊   | 204/300 [07:14<03:13,  2.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 204 — avg: -144.9, min: -194.0, max: -111.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  68%|██████▊   | 205/300 [07:16<03:08,  1.99s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 205 — avg: -144.5, min: -183.0, max: -108.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  69%|██████▊   | 206/300 [07:18<03:27,  2.21s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 206 — avg: -141.8, min: -198.0, max: -108.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  69%|██████▉   | 207/300 [07:20<03:22,  2.17s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 207 — avg: -135.2, min: -180.0, max: -108.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  69%|██████▉   | 208/300 [07:22<03:13,  2.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 208 — avg: -126.6, min: -158.0, max: -103.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  70%|██████▉   | 209/300 [07:24<03:06,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 209 — avg: -124.6, min: -150.0, max: -96.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  70%|███████   | 210/300 [07:26<03:01,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 210 — avg: -123.8, min: -156.0, max: -106.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  70%|███████   | 211/300 [07:28<02:56,  1.99s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 211 — avg: -124.9, min: -175.0, max: -98.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  71%|███████   | 212/300 [07:31<03:12,  2.18s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 212 — avg: -119.8, min: -176.0, max: -88.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  71%|███████   | 213/300 [07:33<03:10,  2.19s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 213 — avg: -125.6, min: -176.0, max: -98.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  71%|███████▏  | 214/300 [07:35<03:01,  2.11s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 214 — avg: -125.9, min: -174.0, max: -103.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  72%|███████▏  | 215/300 [07:37<02:55,  2.06s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 215 — avg: -124.2, min: -183.0, max: -98.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  72%|███████▏  | 216/300 [07:39<02:49,  2.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 216 — avg: -121.0, min: -171.0, max: -86.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  72%|███████▏  | 217/300 [07:41<02:45,  1.99s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 217 — avg: -119.4, min: -168.0, max: -86.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  73%|███████▎  | 218/300 [07:44<02:57,  2.17s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 218 — avg: -114.8, min: -148.0, max: -89.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  73%|███████▎  | 219/300 [07:46<02:57,  2.19s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 219 — avg: -114.8, min: -143.0, max: -83.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  73%|███████▎  | 220/300 [07:47<02:49,  2.11s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 220 — avg: -110.8, min: -144.0, max: -89.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  74%|███████▎  | 221/300 [07:49<02:42,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 221 — avg: -116.2, min: -162.0, max: -89.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  74%|███████▍  | 222/300 [07:51<02:37,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 222 — avg: -109.6, min: -162.0, max: -89.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  74%|███████▍  | 223/300 [07:53<02:32,  1.98s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 223 — avg: -109.5, min: -142.0, max: -83.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  75%|███████▍  | 224/300 [07:56<02:42,  2.14s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 224 — avg: -106.8, min: -140.0, max: -85.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  75%|███████▌  | 225/300 [07:58<02:44,  2.20s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 225 — avg: -107.6, min: -141.0, max: -80.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  75%|███████▌  | 226/300 [08:00<02:36,  2.12s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 226 — avg: -107.3, min: -169.0, max: -78.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  76%|███████▌  | 227/300 [08:02<02:30,  2.06s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 227 — avg: -103.6, min: -155.0, max: -70.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  76%|███████▌  | 228/300 [08:04<02:25,  2.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 228 — avg: -102.4, min: -160.0, max: -65.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  76%|███████▋  | 229/300 [08:06<02:21,  1.99s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 229 — avg: -104.8, min: -180.0, max: -65.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  77%|███████▋  | 230/300 [08:09<02:41,  2.30s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 230 — avg: -99.7, min: -139.0, max: -65.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  77%|███████▋  | 231/300 [08:12<02:51,  2.49s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 231 — avg: -98.0, min: -142.0, max: -65.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  77%|███████▋  | 232/300 [08:14<02:38,  2.32s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 232 — avg: -99.6, min: -142.0, max: -65.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  78%|███████▊  | 233/300 [08:16<02:27,  2.20s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 233 — avg: -102.4, min: -154.0, max: -72.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  78%|███████▊  | 234/300 [08:17<02:19,  2.12s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 234 — avg: -111.3, min: -187.0, max: -73.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  78%|███████▊  | 235/300 [08:19<02:14,  2.06s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 235 — avg: -104.5, min: -147.0, max: -73.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  79%|███████▊  | 236/300 [08:22<02:12,  2.07s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 236 — avg: -99.0, min: -140.0, max: -63.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  79%|███████▉  | 237/300 [08:24<02:22,  2.27s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 237 — avg: -95.8, min: -130.0, max: -73.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  79%|███████▉  | 238/300 [08:26<02:15,  2.18s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 238 — avg: -97.3, min: -131.0, max: -73.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  80%|███████▉  | 239/300 [08:28<02:08,  2.11s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 239 — avg: -99.6, min: -123.0, max: -73.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  80%|████████  | 240/300 [08:30<02:03,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 240 — avg: -93.9, min: -150.0, max: -62.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  80%|████████  | 241/300 [08:32<01:59,  2.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 241 — avg: -89.2, min: -142.0, max: -67.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  81%|████████  | 242/300 [08:34<01:56,  2.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 242 — avg: -83.3, min: -115.0, max: -63.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  81%|████████  | 243/300 [08:37<02:08,  2.25s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 243 — avg: -87.5, min: -121.0, max: -63.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  81%|████████▏ | 244/300 [08:39<02:00,  2.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 244 — avg: -88.8, min: -123.0, max: -66.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  82%|████████▏ | 245/300 [08:41<01:54,  2.09s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 245 — avg: -82.5, min: -113.0, max: -53.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  82%|████████▏ | 246/300 [08:43<01:50,  2.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 246 — avg: -81.8, min: -128.0, max: -53.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  82%|████████▏ | 247/300 [08:45<01:46,  2.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 247 — avg: -77.7, min: -118.0, max: -57.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  83%|████████▎ | 248/300 [08:47<01:43,  1.99s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 248 — avg: -76.0, min: -114.0, max: -61.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  83%|████████▎ | 249/300 [08:49<01:55,  2.26s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 249 — avg: -76.7, min: -126.0, max: -48.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  83%|████████▎ | 250/300 [08:51<01:47,  2.15s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 250 — avg: -75.0, min: -118.0, max: -48.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  84%|████████▎ | 251/300 [08:53<01:42,  2.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 251 — avg: -78.3, min: -162.0, max: -48.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  84%|████████▍ | 252/300 [08:55<01:37,  2.04s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 252 — avg: -77.3, min: -170.0, max: -53.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  84%|████████▍ | 253/300 [08:57<01:34,  2.00s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 253 — avg: -75.5, min: -104.0, max: -58.0\n"
          ]
        },
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          ]
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        {
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          "text": [
            "Gen 255 — avg: -76.3, min: -128.0, max: -57.0\n"
          ]
        },
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        {
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          "text": [
            "Gen 256 — avg: -78.8, min: -142.0, max: -53.0\n"
          ]
        },
        {
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        {
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          "text": [
            "Gen 257 — avg: -74.9, min: -118.0, max: -53.0\n"
          ]
        },
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        {
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          "text": [
            "Gen 258 — avg: -73.2, min: -111.0, max: -53.0\n"
          ]
        },
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        {
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          "text": [
            "Gen 259 — avg: -73.0, min: -117.0, max: -43.0\n"
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        {
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          "text": [
            "Gen 260 — avg: -71.6, min: -103.0, max: -48.0\n"
          ]
        },
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        {
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          "text": [
            "Gen 261 — avg: -72.6, min: -102.0, max: -48.0\n"
          ]
        },
        {
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        {
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          "text": [
            "Gen 262 — avg: -75.6, min: -116.0, max: -48.0\n"
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        },
        {
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        {
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          "text": [
            "Gen 263 — avg: -70.6, min: -107.0, max: -53.0\n"
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        {
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          "text": [
            "Gen 264 — avg: -75.8, min: -115.0, max: -52.0\n"
          ]
        },
        {
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        {
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          "text": [
            "Gen 265 — avg: -74.1, min: -152.0, max: -48.0\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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          "text": [
            "Gen 266 — avg: -67.8, min: -93.0, max: -43.0\n"
          ]
        },
        {
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        },
        {
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          "text": [
            "Gen 267 — avg: -70.7, min: -104.0, max: -43.0\n"
          ]
        },
        {
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        {
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          "text": [
            "Gen 268 — avg: -68.9, min: -91.0, max: -48.0\n"
          ]
        },
        {
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          ]
        },
        {
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          "text": [
            "Gen 269 — avg: -69.3, min: -139.0, max: -33.0\n"
          ]
        },
        {
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        },
        {
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          "text": [
            "Gen 270 — avg: -68.8, min: -126.0, max: -48.0\n"
          ]
        },
        {
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "Gen 271 — avg: -73.3, min: -125.0, max: -48.0\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 272 — avg: -75.0, min: -123.0, max: -53.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "Gen 273 — avg: -73.0, min: -95.0, max: -43.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 274 — avg: -71.0, min: -138.0, max: -43.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "Gen 275 — avg: -74.1, min: -108.0, max: -48.0\n"
          ]
        },
        {
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          "text": [
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "Gen 276 — avg: -75.8, min: -131.0, max: -51.0\n"
          ]
        },
        {
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "Gen 277 — avg: -75.2, min: -128.0, max: -51.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  93%|█████████▎| 278/300 [09:49<00:43,  1.99s/it]"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "Gen 278 — avg: -72.9, min: -118.0, max: -51.0\n"
          ]
        },
        {
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "Gen 279 — avg: -76.4, min: -115.0, max: -48.0\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Evolving:  93%|█████████▎| 280/300 [09:54<00:44,  2.20s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 280 — avg: -77.0, min: -113.0, max: -48.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  94%|█████████▎| 281/300 [09:56<00:40,  2.12s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 281 — avg: -70.9, min: -112.0, max: -48.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  94%|█████████▍| 282/300 [09:58<00:37,  2.06s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 282 — avg: -71.5, min: -115.0, max: -48.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  94%|█████████▍| 283/300 [10:00<00:34,  2.02s/it]"
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        },
        {
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          "name": "stdout",
          "text": [
            "Gen 283 — avg: -71.5, min: -122.0, max: -48.0\n"
          ]
        },
        {
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          "text": [
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 284 — avg: -68.4, min: -98.0, max: -33.0\n"
          ]
        },
        {
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          "text": [
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "Gen 285 — avg: -69.1, min: -101.0, max: -33.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  95%|█████████▌| 286/300 [10:07<00:30,  2.20s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 286 — avg: -65.2, min: -106.0, max: -37.0\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Evolving:  96%|█████████▌| 287/300 [10:08<00:27,  2.12s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 287 — avg: -61.8, min: -103.0, max: -37.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  96%|█████████▌| 288/300 [10:10<00:24,  2.06s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 288 — avg: -61.3, min: -106.0, max: -42.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  96%|█████████▋| 289/300 [10:12<00:22,  2.03s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 289 — avg: -62.9, min: -103.0, max: -42.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  97%|█████████▋| 290/300 [10:14<00:19,  2.00s/it]"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 290 — avg: -62.2, min: -88.0, max: -43.0\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Evolving:  97%|█████████▋| 291/300 [10:17<00:19,  2.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 291 — avg: -63.9, min: -95.0, max: -43.0\n"
          ]
        },
        {
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            "Evolving:  97%|█████████▋| 292/300 [10:19<00:17,  2.21s/it]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 292 — avg: -60.7, min: -112.0, max: -43.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  98%|█████████▊| 293/300 [10:21<00:14,  2.13s/it]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 293 — avg: -63.0, min: -115.0, max: -38.0\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Evolving:  98%|█████████▊| 294/300 [10:23<00:12,  2.07s/it]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 294 — avg: -60.8, min: -90.0, max: -33.0\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Evolving:  98%|█████████▊| 295/300 [10:25<00:10,  2.03s/it]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 295 — avg: -62.1, min: -108.0, max: -38.0\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Evolving:  99%|█████████▊| 296/300 [10:27<00:08,  2.01s/it]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 296 — avg: -62.7, min: -100.0, max: -38.0\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Evolving:  99%|█████████▉| 297/300 [10:30<00:06,  2.13s/it]"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 297 — avg: -67.3, min: -135.0, max: -38.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving:  99%|█████████▉| 298/300 [10:32<00:04,  2.22s/it]"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 298 — avg: -62.0, min: -99.0, max: -43.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving: 100%|█████████▉| 299/300 [10:34<00:02,  2.13s/it]"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gen 299 — avg: -59.1, min: -128.0, max: -43.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evolving: 100%|██████████| 300/300 [10:35<00:00,  2.12s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Plot results ---\n",
        "plt.plot(avg_fitness, label='Average Fitness')\n",
        "plt.plot(min_fitness, label='Min Fitness')\n",
        "plt.plot(max_fitness, label='Max Fitness')\n",
        "plt.xlabel('Generation')\n",
        "plt.ylabel('Fitness')\n",
        "plt.title('GA Fitness over Generations')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "rkXvPGvgweB9",
        "outputId": "cf9b7fbe-51ed-470d-a432-476c28d68a4c"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Problem 2"
      ],
      "metadata": {
        "id": "tcbxvfqUKLoA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def generate_time_dependent_graph(cities):\n",
        "    graph = {}\n",
        "    for from_city in cities:\n",
        "        graph[from_city] = {}\n",
        "        for to_city in cities:\n",
        "            if from_city != to_city:\n",
        "                graph[from_city][to_city] = [random.randint(1, 4) for _ in range(24)]\n",
        "    return graph\n",
        "\n",
        "def get_graph_at_hour(graph, hour):\n",
        "    hourly_graph = nx.DiGraph()\n",
        "    for from_node, edges in graph.items():\n",
        "        for to_node, weights in edges.items():\n",
        "            hourly_graph.add_edge(from_node, to_node, weight=weights[hour])\n",
        "    return hourly_graph\n",
        "\n",
        "def draw_graphs_for_hours(graph, hours):\n",
        "    fig, axes = plt.subplots(1, len(hours), figsize=(16, 4))\n",
        "    pos = nx.spring_layout(nx.DiGraph(graph))  # fixed layout for all graphs\n",
        "\n",
        "    for idx, hour in enumerate(hours):\n",
        "        G_hour = get_graph_at_hour(graph, hour)\n",
        "        ax = axes[idx]\n",
        "        ax.set_title(f'Hour {hour}')\n",
        "        nx.draw(G_hour, pos, ax=ax, with_labels=True, node_color='lightblue', node_size=800, arrows=True)\n",
        "        labels = nx.get_edge_attributes(G_hour, 'weight')\n",
        "        nx.draw_networkx_edge_labels(G_hour, pos, edge_labels=labels, ax=ax, font_size=8)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "# Create the graph and visualize it\n",
        "cities = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M']\n",
        "time_dependent_graph = generate_time_dependent_graph(cities)\n",
        "\n",
        "# Render for 4 sample hours\n",
        "draw_graphs_for_hours(time_dependent_graph, hours=[0, 6, 12, 18])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "KGvH1oIg4H5z",
        "outputId": "9cb98511-f358-4308-fa53-b45c2654efd2"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "def evaluate_genome(genome, graph_data, start_city):\n",
        "    current_time = 0\n",
        "    current_city = start_city\n",
        "    visited = set([current_city])\n",
        "\n",
        "    for next_city in genome:\n",
        "        if next_city == current_city:\n",
        "            continue\n",
        "\n",
        "        graph_at_time = get_graph_at_hour(graph_data, current_time % 24)\n",
        "        if not graph_at_time.has_edge(current_city, next_city):\n",
        "            continue\n",
        "\n",
        "        travel_time = graph_at_time[current_city][next_city]['weight']\n",
        "        current_time += travel_time\n",
        "        current_city = next_city\n",
        "        visited.add(current_city)\n",
        "\n",
        "        if len(visited) == len(graph_data):\n",
        "            break\n",
        "\n",
        "    return -current_time if len(visited) == len(graph_data) else -float('inf')\n",
        "\n",
        "def mutate(genome, cities, mutation_rate=0.1):\n",
        "    new_genome = genome[:]\n",
        "    for i in range(len(new_genome)):\n",
        "        if random.random() < mutation_rate:\n",
        "            new_genome[i] = random.choice(cities)\n",
        "    return new_genome\n",
        "\n",
        "def crossover(parent1, parent2):\n",
        "    return [random.choice([g1, g2]) for g1, g2 in zip(parent1, parent2)]\n",
        "\n",
        "def run_generation(population, graph, cities, start_city, mutation_rate=0.1):\n",
        "    scored_population = [(genome, evaluate_genome(genome, graph, start_city)) for genome in population]\n",
        "    scored_population.sort(key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    next_generation = [genome for genome, _ in scored_population[:2]]  # elitism\n",
        "\n",
        "    while len(next_generation) < len(population):\n",
        "        parent1, parent2 = random.choices(scored_population[:5], k=2)\n",
        "        child = crossover(parent1[0], parent2[0])\n",
        "        mutated_child = mutate(child, cities, mutation_rate)\n",
        "        next_generation.append(mutated_child)\n",
        "\n",
        "    return next_generation, scored_population[0]\n",
        "\n",
        "def genetic_algorithm(graph, cities, start_city='A', generations=10, pop_size=500, mutation_rate=0.1):\n",
        "    genome_length = 2 * len(cities)\n",
        "    population = [\n",
        "        [random.choice(cities) for _ in range(genome_length)]\n",
        "        for _ in range(pop_size)\n",
        "    ]\n",
        "\n",
        "    best_overall = (None, -float('inf'))\n",
        "    for gen in range(generations):\n",
        "        population, best = run_generation(population, graph, cities, start_city, mutation_rate)\n",
        "        if best[1] > best_overall[1]:\n",
        "            best_overall = best\n",
        "        print(f\"Generation {gen+1}: Best fitness = {best[1]}\")\n",
        "\n",
        "    print(\"\\nBest path found:\", best_overall[0])\n",
        "    print(\"Final fitness:\", best_overall[1])\n",
        "    return best_overall\n",
        "\n",
        "# --- Run the GA on your existing graph ---\n",
        "best_path, best_fitness = genetic_algorithm(time_dependent_graph, cities)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GM-cUtUhLdY2",
        "outputId": "1e9a3b3f-a806-4c4b-c0f2-280d84b1b126"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generation 1: Best fitness = -35\n",
            "Generation 2: Best fitness = -30\n",
            "Generation 3: Best fitness = -27\n",
            "Generation 4: Best fitness = -24\n",
            "Generation 5: Best fitness = -24\n",
            "Generation 6: Best fitness = -22\n",
            "Generation 7: Best fitness = -21\n",
            "Generation 8: Best fitness = -21\n",
            "Generation 9: Best fitness = -21\n",
            "Generation 10: Best fitness = -21\n",
            "\n",
            "Best path found: ['L', 'F', 'D', 'K', 'B', 'M', 'C', 'E', 'J', 'G', 'I', 'H', 'F', 'K', 'J', 'J', 'J', 'A', 'L', 'B', 'L', 'E', 'L', 'L', 'G', 'C']\n",
            "Final fitness: -21\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rPn5X5bLNJb2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}